setwd("D:/BINUS/Semester_4/Group 2_Indonesia Visitor Analysis/Group 2_Indonesia Visitor Analysis")
data <- read.csv("visitor_asean.csv")
#data overview
str(data)
## 'data.frame':    907 obs. of  8 variables:
##  $ Destination.Country: chr  "Brunei Darussalam" "Brunei Darussalam" "Brunei Darussalam" "Brunei Darussalam" ...
##  $ Origin.Country     : chr  "Total Intra-ASEAN" "Total Country (World)" "Total EU-27" "Total EU-28" ...
##  $ X2015              : num  118719 218213 7750 18879 9972 ...
##  $ X2016              : num  117525 218809 6941 17301 7989 ...
##  $ X2017              : num  131351 258955 7581 19673 8600 ...
##  $ X2018              : num  134596 278136 7338 19304 9702 ...
##  $ X2019              : num  168705 333244 5884 20082 10188 ...
##  $ X2020              : num  32571 62325 2034 NaN 2597 ...
head(data)
##   Destination.Country        Origin.Country  X2015  X2016  X2017  X2018  X2019
## 1   Brunei Darussalam     Total Intra-ASEAN 118719 117525 131351 134596 168705
## 2   Brunei Darussalam Total Country (World) 218213 218809 258955 278136 333244
## 3   Brunei Darussalam           Total EU-27   7750   6941   7581   7338   5884
## 4   Brunei Darussalam           Total EU-28  18879  17301  19673  19304  20082
## 5   Brunei Darussalam        Australia [AU]   9972   7989   8600   9702  10188
## 6   Brunei Darussalam          Austria [AT]    122    128    101    158    NaN
##   X2020
## 1 32571
## 2 62325
## 3  2034
## 4   NaN
## 5  2597
## 6    18
#missing value
missing_values <- sapply(data, function(x) sum(is.na(x)))
print("Missing values in each feature:")
## [1] "Missing values in each feature:"
print(missing_values)
## Destination.Country      Origin.Country               X2015               X2016 
##                   0                   0                 210                 383 
##               X2017               X2018               X2019               X2020 
##                  55                  85                  97                  75
#data imputation

data_impute <- na.omit(data)
print("Data Table after dropping rows with missing values:")
## [1] "Data Table after dropping rows with missing values:"
print(data_impute)
##     Destination.Country                      Origin.Country    X2015    X2016
## 1     Brunei Darussalam                   Total Intra-ASEAN   118719   117525
## 2     Brunei Darussalam               Total Country (World)   218213   218809
## 3     Brunei Darussalam                         Total EU-27     7750     6941
## 5     Brunei Darussalam                      Australia [AU]     9972     7989
## 8     Brunei Darussalam                     Bangladesh [BD]     1531     2627
## 10    Brunei Darussalam                       Cambodia [KH]      251      318
## 11    Brunei Darussalam                         Canada [CA]     2245     2013
## 12    Brunei Darussalam                          China [CN]    36886    40838
## 14    Brunei Darussalam                        Denmark [DK]      250      272
## 17    Brunei Darussalam                         France [FR]     1700     1340
## 18    Brunei Darussalam                        Germany [DE]     1923     1657
## 21    Brunei Darussalam                          India [IN]     6379     7193
## 22    Brunei Darussalam                      Indonesia [ID]    17152    20844
## 23    Brunei Darussalam                        Ireland [IE]      303      301
## 24    Brunei Darussalam                          Italy [IT]      543      512
## 25    Brunei Darussalam                          Japan [JP]     4336     4474
## 26    Brunei Darussalam                          Korea [KR]     2871     3551
## 28    Brunei Darussalam                        Lao PDR [LA]      125      192
## 29    Brunei Darussalam                       Malaysia [MY]    57986    54374
## 30    Brunei Darussalam                        Myanmar [MM]      618      596
## 31    Brunei Darussalam                          Nepal [NP]     1092     1129
## 32    Brunei Darussalam                    Netherlands [NL]     1348     1020
## 33    Brunei Darussalam                    New Zealand [NZ]     1641     1454
## 34    Brunei Darussalam                         Norway [NO]      348      229
## 35    Brunei Darussalam                           Oman [OM]      366      267
## 36    Brunei Darussalam   Others Unspecified Countries [O0]     3435     3273
## 37    Brunei Darussalam                       Pakistan [PK]      621      656
## 38    Brunei Darussalam                    Philippines [PH]    17922    17064
## 44    Brunei Darussalam                      Singapore [SG]    16230    14473
## 46    Brunei Darussalam                      Sri Lanka [LK]      352      291
## 47    Brunei Darussalam                         Sweden [SE]      292      262
## 50    Brunei Darussalam                       Thailand [TH]     5831     6303
## 53    Brunei Darussalam                 United Kingdom [GB]    11129    10360
## 54    Brunei Darussalam                  United States [US]     3704     3408
## 55    Brunei Darussalam                       Viet Nam [VN]     2604     3361
## 56             Cambodia                   Total Intra-ASEAN  2097758  2121220
## 57             Cambodia               Total Country (World)  4775231  5011712
## 58             Cambodia                         Total EU-27   456880   458431
## 60             Cambodia                    Afghanistan [AF]      121      158
## 61             Cambodia                      Argentina [AR]     7870    13497
## 62             Cambodia                      Australia [AU]   134748   146806
## 63             Cambodia                        Austria [AT]    10080    11203
## 64             Cambodia                     Bangladesh [BD]     2057     1851
## 65             Cambodia                        Belgium [BE]    17452    18047
## 66             Cambodia                         Bhutan [BT]      229      226
## 67             Cambodia                         Brazil [BR]    10618    12526
## 68             Cambodia              Brunei Darussalam [BN]      852     1120
## 69             Cambodia                       Bulgaria [BG]     1771     1837
## 70             Cambodia                       Cameroon [CM]      202      120
## 71             Cambodia                         Canada [CA]    56834    60715
## 72             Cambodia                          Chile [CL]     4968     6208
## 73             Cambodia                          China [CN]   694712   830003
## 74             Cambodia                       Colombia [CO]     2903     3510
## 75             Cambodia                     Costa Rica [CR]      465      581
## 76             Cambodia                        Croatia [HR]     1304     1213
## 77             Cambodia                 Czech Republic [CZ]     5603     5761
## 78             Cambodia                        Denmark [DK]    11553    11462
## 79             Cambodia                        Ecuador [EC]      560      612
## 80             Cambodia                          Egypt [EG]      587      696
## 81             Cambodia                        Estonia [EE]     1952     1889
## 82             Cambodia                           Fiji [FJ]      191      230
## 83             Cambodia                        Finland [FI]     7246     6639
## 84             Cambodia                         France [FR]   145724   150294
## 85             Cambodia                        Germany [DE]    94040   108784
## 86             Cambodia                          Ghana [GH]       93      190
## 87             Cambodia                         Greece [GR]     1595     1652
## 88             Cambodia                       Honduras [HN]      112      191
## 89             Cambodia                  Hong Kong SAR [HK]    14787    15980
## 90             Cambodia                        Hungary [HU]     4190     4682
## 91             Cambodia                        Iceland [IS]      727      815
## 92             Cambodia                          India [IN]    36671    46131
## 93             Cambodia                      Indonesia [ID]    43147    48771
## 94             Cambodia                           Iraq [IQ]      169      203
## 95             Cambodia                        Ireland [IE]    11200    11943
## 96             Cambodia       Islamic Republic of Iran [IR]      525      600
## 97             Cambodia                         Israel [IL]     9557    11180
## 98             Cambodia                          Italy [IT]    32177    35794
## 99             Cambodia                        Jamaica [JM]      143      108
## 100            Cambodia                          Japan [JP]   193330   191577
## 101            Cambodia                         Jordan [JO]      340     1030
## 102            Cambodia                     Kazakhstan [KZ]     2235     1772
## 103            Cambodia                          Kenya [KE]      344      417
## 104            Cambodia                          Korea [KR]   395259   357194
## 105            Cambodia                         Kuwait [KW]      916      728
## 106            Cambodia                Kyrgyz Republic [KG]      161      175
## 107            Cambodia                        Lao PDR [LA]   405359   369335
## 108            Cambodia                         Latvia [LV]     1081     1023
## 109            Cambodia                        Liberia [LR]       55       46
## 110            Cambodia                      Lithuania [LT]     1861     2211
## 111            Cambodia                     Luxembourg [LU]      690      859
## 112            Cambodia                      Macao SAR [MO]      392      404
## 113            Cambodia                       Malaysia [MY]   149389   152843
## 114            Cambodia                          Malta [MT]      462      512
## 115            Cambodia                         Mexico [MX]     7128     8301
## 116            Cambodia                         Monaco [MC]       89       78
## 117            Cambodia                       Mongolia [MN]      458      478
## 118            Cambodia                        Morocco [MA]      505      509
## 119            Cambodia                        Myanmar [MM]     8965    12819
## 120            Cambodia                          Nepal [NP]     2358     2505
## 121            Cambodia                    Netherlands [NL]    29015    30885
## 122            Cambodia                    New Zealand [NZ]    23021    24797
## 123            Cambodia                        Nigeria [NG]     1069     1507
## 124            Cambodia                         Norway [NO]     9521     9666
## 125            Cambodia Occupied Palestinian Territory [PS]       84      103
## 126            Cambodia                           Oman [OM]      214      212
## 127            Cambodia   Others Unspecified Countries [O0]    18037    65755
## 128            Cambodia                       Pakistan [PK]     1521     1627
## 129            Cambodia                           Peru [PE]     1060     1679
## 130            Cambodia                    Philippines [PH]    84677   108032
## 131            Cambodia                         Poland [PL]    14008    15912
## 132            Cambodia                       Portugal [PT]     5947     7526
## 133            Cambodia                          Qatar [QA]      117       87
## 134            Cambodia              Republic of Congo [CG]      107      110
## 135            Cambodia                        Romania [RO]     2414     2620
## 136            Cambodia                         Russia [RU]    55500    53164
## 137            Cambodia                   Saudi Arabia [SA]      307      349
## 138            Cambodia          Serbia and Montenegro [ZB]      863     1052
## 139            Cambodia                      Singapore [SG]    67669    70556
## 140            Cambodia                Slovak Republic [SK]     2266     2740
## 141            Cambodia                       Slovenia [SI]     1257     1218
## 142            Cambodia                   South Africa [ZA]     5336     5743
## 143            Cambodia                          Spain [ES]    34847     4047
## 144            Cambodia                      Sri Lanka [LK]     1933     2412
## 145            Cambodia                          Sudan [SD]      131      123
## 146            Cambodia                         Sweden [SE]    17145    17678
## 147            Cambodia                    Switzerland [CH]    21979    26582
## 148            Cambodia                          Syria [SY]      260      629
## 149            Cambodia       Taiwan Province of China [TW]   109727   104765
## 150            Cambodia                       Thailand [TH]   349908   398081
## 152            Cambodia                        Tunisia [TN]      207      392
## 153            Cambodia                         Turkey [TR]     5802     6623
## 154            Cambodia                        Ukraine [UA]     5322     5414
## 155            Cambodia           United Arab Emirates [AE]      367      333
## 156            Cambodia                 United Kingdom [GB]   154265   159489
## 157            Cambodia                  United States [US]   217510   238658
## 158            Cambodia                        Uruguay [UY]     1004     1166
## 159            Cambodia                     Uzbekistan [UZ]      735      491
## 160            Cambodia                      Venezuela [VE]      723      469
## 161            Cambodia                       Viet Nam [VN]   987792   959663
## 162            Cambodia                          Yemen [YE]      452      313
## 163           Indonesia                   Total Intra-ASEAN  3860652  3817500
## 164           Indonesia               Total Country (World) 10406759 11519275
## 165           Indonesia                         Total EU-27   883697  1049790
## 178           Indonesia                      Australia [AU]  1099058  1302292
## 179           Indonesia                        Austria [AT]    22791    24375
## 181           Indonesia                        Bahrain [BH]     1687     2243
## 182           Indonesia                     Bangladesh [BD]    16162    39026
## 184           Indonesia                        Belgium [BE]    38842    43607
## 192           Indonesia              Brunei Darussalam [BN]    18304    23693
## 199           Indonesia                         Canada [CA]    75816    86804
## 203           Indonesia                          China [CN]  1260700  1556771
## 215           Indonesia                        Denmark [DK]    28020    36380
## 220           Indonesia                          Egypt [EG]    13102    19948
## 227           Indonesia                        Finland [FI]    19029    21031
## 228           Indonesia                         France [FR]   212575   256229
## 233           Indonesia                        Germany [DE]   203611   243873
## 245           Indonesia                  Hong Kong SAR [HK]    94109   101369
## 248           Indonesia                          India [IN]   319608   422045
## 254           Indonesia                          Italy [IT]    69427    79424
## 256           Indonesia                          Japan [JP]   549705   545392
## 261           Indonesia                          Korea [KR]   387473   386789
## 262           Indonesia                         Kuwait [KW]     8320     6368
## 276           Indonesia                       Malaysia [MY]  1458593  1541197
## 291           Indonesia                        Myanmar [MM]    40635    44721
## 295           Indonesia                    Netherlands [NL]   175317   200811
## 297           Indonesia                    New Zealand [NZ]    87923   105393
## 301           Indonesia                         Norway [NO]    18842    19478
## 304           Indonesia   Others Unspecified Countries [O0]   580132   709585
## 305           Indonesia                       Pakistan [PK]     7976    10098
## 311           Indonesia                    Philippines [PH]   273630   298910
## 313           Indonesia                       Portugal [PT]    22206    29286
## 315           Indonesia                          Qatar [QA]     1608     1856
## 318           Indonesia                         Russia [RU]    72707    88520
## 323           Indonesia                   Saudi Arabia [SA]   164778   197681
## 328           Indonesia                      Singapore [SG]  1624058  1515699
## 332           Indonesia                   South Africa [ZA]    23052    29229
## 333           Indonesia                          Spain [ES]    53891    68840
## 334           Indonesia                      Sri Lanka [LK]    11496    24256
## 341           Indonesia                         Sweden [SE]    37988    45934
## 342           Indonesia                    Switzerland [CH]    52309    56700
## 344           Indonesia       Taiwan Province of China [TW]   227912   252849
## 346           Indonesia                       Thailand [TH]   120879   124569
## 358           Indonesia           United Arab Emirates [AE]    10325     9016
## 359           Indonesia                 United Kingdom [GB]   292745   352017
## 360           Indonesia                  United States [US]   276027   316782
## 365           Indonesia                       Viet Nam [VN]    50165    60984
## 367           Indonesia                          Yemen [YE]     8838     9478
## 368             Lao PDR                   Total Intra-ASEAN  3588538  3083383
## 369             Lao PDR               Total Country (World)  4684429  4239047
## 370             Lao PDR                         Total EU-27   132369   136579
## 372             Lao PDR                      Australia [AU]    34665    33077
## 373             Lao PDR                        Austria [AT]     3910     5324
## 374             Lao PDR                        Belgium [BE]     5289     5682
## 375             Lao PDR              Brunei Darussalam [BN]      865      484
## 376             Lao PDR                       Cambodia [KH]    20625    16536
## 377             Lao PDR                         Canada [CA]    19785    19315
## 378             Lao PDR                          China [CN]   511436   545493
## 379             Lao PDR                        Denmark [DK]     4491     4479
## 380             Lao PDR                        Finland [FI]     3013     3218
## 381             Lao PDR                         France [FR]    55151    54953
## 382             Lao PDR                        Germany [DE]    31897    34018
## 383             Lao PDR                         Greece [GR]      878      593
## 384             Lao PDR                          India [IN]     5492     8249
## 385             Lao PDR                      Indonesia [ID]     6019     5010
## 386             Lao PDR                         Israel [IL]     4163     3593
## 387             Lao PDR                          Italy [IT]     8990    10052
## 388             Lao PDR                          Japan [JP]    43826    49191
## 389             Lao PDR                          Korea [KR]   165328   173260
## 390             Lao PDR                       Malaysia [MY]    24095    24391
## 391             Lao PDR                        Myanmar [MM]     2661     3695
## 392             Lao PDR                    Netherlands [NL]     8429     7004
## 393             Lao PDR                    New Zealand [NZ]     4798     4787
## 394             Lao PDR                         Norway [NO]     3499     4018
## 395             Lao PDR   Others Unspecified Countries [O0]    37524    43197
## 396             Lao PDR                    Philippines [PH]    16709    16750
## 397             Lao PDR                         Russia [RU]    12532    13033
## 398             Lao PDR                      Singapore [SG]     8258     8512
## 399             Lao PDR                          Spain [ES]     4856     5461
## 400             Lao PDR                         Sweden [SE]     5465     5795
## 401             Lao PDR                    Switzerland [CH]     9777    10603
## 402             Lao PDR       Taiwan Province of China [TW]     6131    14005
## 403             Lao PDR                       Thailand [TH]  2321352  2009605
## 404             Lao PDR                 United Kingdom [GB]    41508    39170
## 405             Lao PDR                  United States [US]    63058    58094
## 406             Lao PDR                       Viet Nam [VN]  1187954   998400
## 407            Malaysia                   Total Intra-ASEAN 19146514 20271144
## 408            Malaysia               Total Country (World) 25721251 26757392
## 409            Malaysia                         Total EU-27   585835   514230
## 412            Malaysia                      Australia [AU]   486948   377727
## 416            Malaysia                        Belgium [BE]    18789    14283
## 418            Malaysia              Brunei Darussalam [BN]  1133555  1391016
## 419            Malaysia                       Cambodia [KH]    75059    61844
## 420            Malaysia                         Canada [CA]    79557    72337
## 421            Malaysia                          China [CN]  1677163  2124942
## 423            Malaysia                        Denmark [DK]    24113    21612
## 424            Malaysia                          Egypt [EG]    25637    30231
## 426            Malaysia                         France [FR]   151474   134257
## 427            Malaysia                        Germany [DE]   144910   130276
## 429            Malaysia                      Indonesia [ID]  2788033  3049964
## 431            Malaysia                        Ireland [IE]    22746    18208
## 433            Malaysia                          Italy [IT]    51946    42747
## 434            Malaysia                          Japan [JP]   483569   413768
## 437            Malaysia                          Korea [KR]   421161   444439
## 439            Malaysia                        Lao PDR [LA]    24448    31061
## 444            Malaysia                        Myanmar [MM]    66553    49175
## 447            Malaysia                    Netherlands [NL]    84584    72200
## 448            Malaysia                    New Zealand [NZ]    60846    53352
## 451            Malaysia                         Norway [NO]    18622    14709
## 453            Malaysia   Others Unspecified Countries [O0]   320900  1223374
## 457            Malaysia                    Philippines [PH]   554917   417446
## 461            Malaysia                         Russia [RU]    55263    50893
## 462            Malaysia                   Saudi Arabia [SA]    99754   123878
## 463            Malaysia                      Singapore [SG] 12930754 13272961
## 464            Malaysia                   South Africa [ZA]    20625    20053
## 465            Malaysia                          Spain [ES]    36692    28018
## 468            Malaysia                         Sweden [SE]    35586    32861
## 469            Malaysia                    Switzerland [CH]    28141    26628
## 471            Malaysia       Taiwan Province of China [TW]   283224   300861
## 472            Malaysia                       Thailand [TH]  1343569  1780800
## 474            Malaysia                         Turkey [TR]    15395    13029
## 476            Malaysia           United Arab Emirates [AE]    15769    14150
## 477            Malaysia                 United Kingdom [GB]   401019   400269
## 478            Malaysia                  United States [US]   237768   217075
## 481            Malaysia                       Viet Nam [VN]   229626   216877
## 482             Myanmar                   Total Intra-ASEAN  1762886   402862
## 483             Myanmar               Total Country (World)  4681020  2907207
## 484             Myanmar                         Total EU-27   156453   148672
## 494             Myanmar                      Australia [AU]    30820    34010
## 495             Myanmar                        Austria [AT]     4398     4857
## 500             Myanmar                        Belgium [BE]     6666     7783
## 515             Myanmar                         Canada [CA]    14051    15024
## 519             Myanmar                          China [CN]  2102677   183886
## 544             Myanmar                         France [FR]    47435    52304
## 547             Myanmar                        Germany [DE]    35727    39044
## 563             Myanmar                          India [IN]    59692    38537
## 569             Myanmar                          Italy [IT]    14841    17969
## 571             Myanmar                          Japan [JP]    90312   100784
## 575             Myanmar                          Korea [KR]    63715    64397
## 590             Myanmar                       Malaysia [MY]    40852    43931
## 606             Myanmar                    Netherlands [NL]    12174    13950
## 607             Myanmar                    New Zealand [NZ]     4547     5026
## 621             Myanmar                    Philippines [PH]    19075    16421
## 629             Myanmar                         Russia [RU]     4138     5487
## 639             Myanmar                      Singapore [SG]    45125    50198
## 644             Myanmar                          Spain [ES]     9158    12765
## 653             Myanmar                    Switzerland [CH]    12293    13897
## 655             Myanmar       Taiwan Province of China [TW]    31735    36118
## 657             Myanmar                       Thailand [TH]  1604212   243443
## 669             Myanmar                 United Kingdom [GB]    45120    51051
## 670             Myanmar                  United States [US]    69815    76502
## 675             Myanmar                       Viet Nam [VN]    31150    48869
## 677         Philippines                   Total Intra-ASEAN   481567   461698
## 678         Philippines               Total Country (World)  5360682  5967005
## 679         Philippines                         Total EU-27   292723   341423
## 681         Philippines                        Andorra [AD]      329      364
## 682         Philippines                      Argentina [AR]     1745     2369
## 683         Philippines                      Australia [AU]   241187   251098
## 684         Philippines                        Austria [AT]    12267    13087
## 685         Philippines                        Bahrain [BH]     3562     3689
## 686         Philippines                     Bangladesh [BD]     4664     4516
## 687         Philippines                        Belgium [BE]    12825    14477
## 688         Philippines                         Brazil [BR]     3495     4520
## 689         Philippines              Brunei Darussalam [BN]     9015     8211
## 690         Philippines                       Cambodia [KH]     3503     3526
## 691         Philippines                         Canada [CA]   156363   175631
## 692         Philippines                          China [CN]   490841   675663
## 693         Philippines                       Colombia [CO]     1228     1431
## 694         Philippines                        Denmark [DK]    15269    18049
## 695         Philippines                          Egypt [EG]     1630     1985
## 696         Philippines                        Finland [FI]     6548     6318
## 697         Philippines                         France [FR]    45505    55384
## 698         Philippines                        Germany [DE]    75348    86363
## 699         Philippines                         Greece [GR]     2386     2483
## 700         Philippines                           Guam [GU]    35262    38777
## 701         Philippines                  Hong Kong SAR [HK]   122180   116328
## 702         Philippines                          India [IN]    74824    90816
## 703         Philippines                      Indonesia [ID]    48178    44348
## 704         Philippines                        Ireland [IE]    14050    16557
## 705         Philippines       Islamic Republic of Iran [IR]     2505     2720
## 706         Philippines                         Israel [IL]    11756    16725
## 707         Philippines                          Italy [IT]    21620    25945
## 708         Philippines                          Japan [JP]   495662   535238
## 709         Philippines                         Jordan [JO]      677      718
## 710         Philippines                          Korea [KR]  1339678  1475081
## 711         Philippines                         Kuwait [KW]     5440     6649
## 712         Philippines                        Lao PDR [LA]     1231     1173
## 713         Philippines                     Luxembourg [LU]      687      833
## 714         Philippines                      Macao SAR [MO]    10160     9247
## 715         Philippines                       Malaysia [MY]   155814   139133
## 716         Philippines                         Mexico [MX]     3145     2924
## 717         Philippines                        Myanmar [MM]     7033     7442
## 718         Philippines                          Nauru [NR]       20       17
## 719         Philippines                          Nepal [NP]     2908     3449
## 720         Philippines                    Netherlands [NL]    28632    31876
## 721         Philippines                    New Zealand [NZ]    20579    23431
## 722         Philippines                        Nigeria [NG]     1430     1489
## 723         Philippines                         Norway [NO]    20968    21606
## 725         Philippines   Others Unspecified Countries [O0]   271037   257991
## 726         Philippines                       Pakistan [PK]     4346     4256
## 727         Philippines               Papua New Guinea [PG]     5910     7738
## 728         Philippines                           Peru [PE]      561      581
## 729         Philippines                         Poland [PL]     8030     8893
## 730         Philippines                       Portugal [PT]     2206     2999
## 731         Philippines                          Qatar [QA]     4472     4745
## 732         Philippines                         Russia [RU]    25278    28210
## 733         Philippines                   Saudi Arabia [SA]    50884    56081
## 734         Philippines                      Singapore [SG]   181176   176057
## 735         Philippines                   South Africa [ZA]     4570     5117
## 736         Philippines                          Spain [ES]    24144    32097
## 737         Philippines                      Sri Lanka [LK]     5292     5410
## 738         Philippines                         Sweden [SE]    23206    26062
## 739         Philippines                    Switzerland [CH]    27200    29420
## 740         Philippines       Taiwan Province of China [TW]   177670   229303
## 741         Philippines                       Thailand [TH]    44038    47913
## 742         Philippines                         Turkey [TR]     6026     7884
## 743         Philippines           United Arab Emirates [AE]    16881    17634
## 744         Philippines                 United Kingdom [GB]   154589   173299
## 745         Philippines                  United States [US]   779217   869463
## 746         Philippines                      Venezuela [VE]      221      271
## 747         Philippines                       Viet Nam [VN]    31579    33895
## 748           Singapore                   Total Intra-ASEAN  5748155  5936364
## 749           Singapore               Total Country (World) 15231469 16403595
## 750           Singapore                         Total EU-27   842294   886275
## 752           Singapore                      Australia [AU]  1043568  1027314
## 754           Singapore                     Bangladesh [BD]   122587   123343
## 756           Singapore              Brunei Darussalam [BN]    73594    69891
## 758           Singapore                         Canada [CA]    96247    98474
## 759           Singapore                          China [CN]  2106164  2863669
## 760           Singapore                        Denmark [DK]    31389    31246
## 761           Singapore                          Egypt [EG]     4538     4426
## 762           Singapore                        Finland [FI]    27904    28642
## 763           Singapore                         France [FR]   157483   170913
## 764           Singapore                        Germany [DE]   286732   328765
## 766           Singapore                  Hong Kong SAR [HK]   609888   537970
## 767           Singapore                          India [IN]  1013986  1097200
## 768           Singapore                      Indonesia [ID]  2731690  2893646
## 769           Singapore                        Ireland [IE]    17347    18943
## 770           Singapore       Islamic Republic of Iran [IR]    12267    22641
## 771           Singapore                         Israel [IL]    15743    18635
## 772           Singapore                          Italy [IT]    69350    74629
## 773           Singapore                          Japan [JP]   789179   783863
## 774           Singapore                          Korea [KR]   577082   566510
## 775           Singapore                         Kuwait [KW]    10767    11123
## 777           Singapore                       Malaysia [MY]  1171077  1151584
## 778           Singapore                      Mauritius [MU]     8739    12832
## 779           Singapore                        Myanmar [MM]   105452   113616
## 781           Singapore                    Netherlands [NL]    79052    82245
## 782           Singapore                    New Zealand [NZ]   127618   121100
## 783           Singapore                         Norway [NO]    31650    29250
## 784           Singapore   Others Unspecified Countries [O0]   278200   395117
## 785           Singapore                       Pakistan [PK]    18948    22213
## 786           Singapore                    Philippines [PH]   673374   691643
## 788           Singapore                         Russia [RU]    63842    70404
## 789           Singapore                   Saudi Arabia [SA]    16091    14520
## 790           Singapore                   South Africa [ZA]    32470    32834
## 791           Singapore                          Spain [ES]    48074    49491
## 792           Singapore                      Sri Lanka [LK]    93052   101905
## 793           Singapore                         Sweden [SE]    42562    43765
## 794           Singapore                    Switzerland [CH]   100849   101455
## 795           Singapore       Taiwan Province of China [TW]   378026   394216
## 796           Singapore                       Thailand [TH]   516409   546555
## 798           Singapore           United Arab Emirates [AE]    78693    80234
## 799           Singapore                 United Kingdom [GB]   473810   489224
## 800           Singapore                  United States [US]   499509   516454
## 801           Singapore                       Viet Nam [VN]   418266   469429
## 802            Thailand                   Total Intra-ASEAN  7886136  8897291
## 803            Thailand               Total Country (World) 29881091 32529588
## 804            Thailand                         Total EU-27  2880089  3002656
## 806            Thailand                      Argentina [AR]    28965    46390
## 807            Thailand                      Australia [AU]   805946   813017
## 808            Thailand                        Austria [AT]    97806    97989
## 809            Thailand                     Bangladesh [BD]   107394   103616
## 810            Thailand                        Belgium [BE]   106100   112140
## 811            Thailand                         Brazil [BR]    48522    63139
## 812            Thailand              Brunei Darussalam [BN]    13833    17994
## 813            Thailand                       Cambodia [KH]   487487   684836
## 814            Thailand                         Canada [CA]   227306   222358
## 815            Thailand                          China [CN]  7934791  8821148
## 816            Thailand                        Denmark [DK]   159425   163406
## 817            Thailand                          Egypt [EG]    25216    24913
## 818            Thailand                        Finland [FI]   134731   131207
## 819            Thailand                         France [FR]   681097   697738
## 820            Thailand                        Germany [DE]   760604   825496
## 821            Thailand                  Hong Kong SAR [HK]   669165   690465
## 822            Thailand                          India [IN]  1069149  1076970
## 823            Thailand                      Indonesia [ID]   469226   558499
## 824            Thailand                         Israel [IL]   141021   161254
## 825            Thailand                          Italy [IT]   246066   248903
## 826            Thailand                          Japan [JP]  1381690  1416903
## 827            Thailand                          Korea [KR]  1372995  1449617
## 828            Thailand                         Kuwait [KW]    66772    68168
## 829            Thailand                        Lao PDR [LA]  1233138  1414916
## 830            Thailand                       Malaysia [MY]  3423397  3506199
## 831            Thailand                        Myanmar [MM]   259678   363871
## 832            Thailand                          Nepal [NP]    32678    43170
## 833            Thailand                    Netherlands [NL]   221657   228443
## 834            Thailand                    New Zealand [NZ]   112314    99402
## 835            Thailand                         Norway [NO]   135347   134335
## 836            Thailand   Others Unspecified Countries [O0]  1124238  1308314
## 837            Thailand                       Pakistan [PK]    78619    71720
## 838            Thailand                    Philippines [PH]   310975   323860
## 839            Thailand                         Russia [RU]   884085  1085890
## 840            Thailand                   Saudi Arabia [SA]    19163    33038
## 841            Thailand                      Singapore [SG]   937311  1163309
## 843            Thailand                          Spain [ES]   150940   168264
## 844            Thailand                      Sri Lanka [LK]    75429    67876
## 845            Thailand                         Sweden [SE]   321663   329070
## 846            Thailand                    Switzerland [CH]   206454   226412
## 847            Thailand       Taiwan Province of China [TW]   552624   513528
## 848            Thailand           United Arab Emirates [AE]   124719   187665
## 849            Thailand                 United Kingdom [GB]   946919   961471
## 850            Thailand                  United States [US]   867520   938862
## 851            Thailand                       Viet Nam [VN]   751091   863807
## 852            Viet Nam                   Total Intra-ASEAN  1300839  1461172
## 853            Viet Nam               Total Country (World)  7943651 10012735
## 854            Viet Nam                         Total EU-27   598205   701616
## 857            Viet Nam                      Australia [AU]   303721   320678
## 860            Viet Nam                        Belgium [BE]    23939    26231
## 863            Viet Nam                       Cambodia [KH]   227074   211949
## 864            Viet Nam                         Canada [CA]   105670   122929
## 865            Viet Nam                          China [CN]  1780918  2696848
## 868            Viet Nam                        Denmark [DK]    28293    30996
## 869            Viet Nam                        Finland [FI]    15043    15953
## 870            Viet Nam                         France [FR]   211636   240808
## 871            Viet Nam                        Germany [DE]   149079   176015
## 874            Viet Nam                      Indonesia [ID]    62240    69653
## 877            Viet Nam                          Italy [IT]    40291    51265
## 878            Viet Nam                          Japan [JP]   671379   740592
## 880            Viet Nam                          Korea [KR]  1112978  1543883
## 881            Viet Nam                        Lao PDR [LA]   113992   137004
## 883            Viet Nam                       Malaysia [MY]   346584   407574
## 887            Viet Nam                    Netherlands [NL]    52967    64712
## 888            Viet Nam                    New Zealand [NZ]    31960    42588
## 889            Viet Nam                         Norway [NO]    21425    23110
## 890            Viet Nam   Others Unspecified Countries [O0]   299075   544458
## 892            Viet Nam                    Philippines [PH]    99757   110967
## 894            Viet Nam                         Russia [RU]   338843   433987
## 896            Viet Nam                      Singapore [SG]   236547   257041
## 897            Viet Nam                          Spain [ES]    44932    57957
## 899            Viet Nam                         Sweden [SE]    32025    37679
## 900            Viet Nam                    Switzerland [CH]    28750    31475
## 901            Viet Nam       Taiwan Province of China [TW]   330196   507301
## 902            Viet Nam                       Thailand [TH]   214645   266984
## 905            Viet Nam                 United Kingdom [GB]   212798   254841
## 906            Viet Nam                  United States [US]   368190   552644
##        X2017    X2018    X2019   X2020
## 1     131351   134596   168705   32571
## 2     258955   278136   333244   62325
## 3       7581     7338     5884    2034
## 5       8600     9702    10188    2597
## 8       3553     3878     3281     638
## 10       356      420      463      29
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## 12     52391    65563    74511   11329
## 14       251      165      312      51
## 17      1403     1318     1381     234
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## 21      8691     8635     8925    1750
## 22     22420    27462    33626    6262
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## 26      8705     9125    15767    1939
## 28       144      233      221      46
## 29     60030    59528    82876   16869
## 30       643      667      872      75
## 31      1861     2308     1926     499
## 32      1301     1218     1360     330
## 33      1394     1385     1272     229
## 34       244      266      280      45
## 35       298      277      218      41
## 36      3854     4113     9581     825
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## 718       16       38       93       5
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## 902   301587   349310   509802  125725
## 905   283537   298114   315084   81433
## 906   614117   687226   746171  172706
missing_values1 <- sapply(data_impute, function(x) sum(is.na(x)))
print("Missing values in each feature after imputation:")
## [1] "Missing values in each feature after imputation:"
print(missing_values1)
## Destination.Country      Origin.Country               X2015               X2016 
##                   0                   0                   0                   0 
##               X2017               X2018               X2019               X2020 
##                   0                   0                   0                   0
#Isolate indonesian as the main source destination country.
library(dplyr)
# Filter the data to isolate rows where the 'Destination Country' is 'Indonesia'
indonesia_data1 <- filter(data_impute, `Destination.Country` == "Indonesia")
indonesia_data <- indonesia_data1[!indonesia_data1$Origin.Country %in% c("Total Intra-ASEAN", "Total Country (World)", "Total EU-27"), ]
# Print the filtered data
print(indonesia_data)
##    Destination.Country                    Origin.Country   X2015   X2016
## 4            Indonesia                    Australia [AU] 1099058 1302292
## 5            Indonesia                      Austria [AT]   22791   24375
## 6            Indonesia                      Bahrain [BH]    1687    2243
## 7            Indonesia                   Bangladesh [BD]   16162   39026
## 8            Indonesia                      Belgium [BE]   38842   43607
## 9            Indonesia            Brunei Darussalam [BN]   18304   23693
## 10           Indonesia                       Canada [CA]   75816   86804
## 11           Indonesia                        China [CN] 1260700 1556771
## 12           Indonesia                      Denmark [DK]   28020   36380
## 13           Indonesia                        Egypt [EG]   13102   19948
## 14           Indonesia                      Finland [FI]   19029   21031
## 15           Indonesia                       France [FR]  212575  256229
## 16           Indonesia                      Germany [DE]  203611  243873
## 17           Indonesia                Hong Kong SAR [HK]   94109  101369
## 18           Indonesia                        India [IN]  319608  422045
## 19           Indonesia                        Italy [IT]   69427   79424
## 20           Indonesia                        Japan [JP]  549705  545392
## 21           Indonesia                        Korea [KR]  387473  386789
## 22           Indonesia                       Kuwait [KW]    8320    6368
## 23           Indonesia                     Malaysia [MY] 1458593 1541197
## 24           Indonesia                      Myanmar [MM]   40635   44721
## 25           Indonesia                  Netherlands [NL]  175317  200811
## 26           Indonesia                  New Zealand [NZ]   87923  105393
## 27           Indonesia                       Norway [NO]   18842   19478
## 28           Indonesia Others Unspecified Countries [O0]  580132  709585
## 29           Indonesia                     Pakistan [PK]    7976   10098
## 30           Indonesia                  Philippines [PH]  273630  298910
## 31           Indonesia                     Portugal [PT]   22206   29286
## 32           Indonesia                        Qatar [QA]    1608    1856
## 33           Indonesia                       Russia [RU]   72707   88520
## 34           Indonesia                 Saudi Arabia [SA]  164778  197681
## 35           Indonesia                    Singapore [SG] 1624058 1515699
## 36           Indonesia                 South Africa [ZA]   23052   29229
## 37           Indonesia                        Spain [ES]   53891   68840
## 38           Indonesia                    Sri Lanka [LK]   11496   24256
## 39           Indonesia                       Sweden [SE]   37988   45934
## 40           Indonesia                  Switzerland [CH]   52309   56700
## 41           Indonesia     Taiwan Province of China [TW]  227912  252849
## 42           Indonesia                     Thailand [TH]  120879  124569
## 43           Indonesia         United Arab Emirates [AE]   10325    9016
## 44           Indonesia               United Kingdom [GB]  292745  352017
## 45           Indonesia                United States [US]  276027  316782
## 46           Indonesia                     Viet Nam [VN]   50165   60984
## 47           Indonesia                        Yemen [YE]    8838    9478
##      X2017   X2018   X2019  X2020
## 4  1256927 1301478 1386803 256291
## 5    27208   29492   28476   4858
## 6     2457    2324    2631    373
## 7    56503   56564   59777  12866
## 8    48477   50050   46780   5902
## 9    23455   17279   19278   2701
## 10   96139   97908  103616  23200
## 11 2093171 2139161 2072252 239768
## 12   43721   46825   45090  10533
## 13   20345   18075   21354   4337
## 14   24447   27127   22665   6376
## 15  274117  287917  283814  43438
## 16  267823  274166  277653  46361
## 17   98272   91182   50324   2625
## 18  536902  595636  657300 111724
## 19   90022   94288   91229  13260
## 20  573310  530573  519623  92228
## 21  423191  358885  388316  75562
## 22    5760    5551    5762    846
## 23 2121888 2503344 2980753 980118
## 24   48133   28612   46381  12669
## 25  210426  209978  215287  53495
## 26  106914  128366  149010  19947
## 27   22838   24906   23886   5072
## 28    7455    6340    6573   1412
## 29   11424   13448   14663   4110
## 30  308977  217874  260980  50413
## 31   33223   36804   35434   6245
## 32    1859    2104    1989    225
## 33  117532  125728  158943  67491
## 34  182086  165912  157512  31906
## 35 1554119 1768744 1934445 280492
## 36   38073   41962   47657   7350
## 37   81690   85560   83373  11829
## 38   35669   32508   28907   4300
## 39   51417   50381   56402  17600
## 40   61191   60293   57484   8362
## 41  264278  208317  207490  35680
## 42  138235  124153  136699  21303
## 43    8387    7100    9065   1093
## 44  378131  392112  397684  69997
## 45  344766  387856  457832  91782
## 46   77466   75816   96024  19608
## 47    8453   10008    9221   2094
# Alternatively, save the filtered data to a new CSV file
# Reshape the data to a long format
indonesiaData_long <- indonesia_data %>%
  pivot_longer(cols = starts_with("X"), names_to = "Year", values_to = "Visitors") %>%
  mutate(Year = as.numeric(sub("X", "", Year)))
print(indonesiaData_long)
## # A tibble: 264 × 4
##    Destination.Country Origin.Country  Year Visitors
##    <chr>               <chr>          <dbl>    <dbl>
##  1 Indonesia           Australia [AU]  2015  1099058
##  2 Indonesia           Australia [AU]  2016  1302292
##  3 Indonesia           Australia [AU]  2017  1256927
##  4 Indonesia           Australia [AU]  2018  1301478
##  5 Indonesia           Australia [AU]  2019  1386803
##  6 Indonesia           Australia [AU]  2020   256291
##  7 Indonesia           Austria [AT]    2015    22791
##  8 Indonesia           Austria [AT]    2016    24375
##  9 Indonesia           Austria [AT]    2017    27208
## 10 Indonesia           Austria [AT]    2018    29492
## # ℹ 254 more rows
# Count the visitors by origin country
origin_country_counts <- indonesiaData_long %>%
  group_by(Origin.Country) %>%
  summarize(TotalVisitors = sum(Visitors, na.rm = TRUE)) %>%
  arrange(desc(TotalVisitors))
# Print the counts
print(origin_country_counts)
## # A tibble: 44 × 2
##    Origin.Country      TotalVisitors
##    <chr>                       <dbl>
##  1 Malaysia [MY]            11585893
##  2 China [CN]                9361823
##  3 Singapore [SG]            8677557
##  4 Australia [AU]            6602849
##  5 Japan [JP]                2810831
##  6 India [IN]                2643215
##  7 Korea [KR]                2020216
##  8 United Kingdom [GB]       1882686
##  9 United States [US]        1875045
## 10 Philippines [PH]          1410784
## # ℹ 34 more rows
# Filter to get the top 10 origin countries
top_10_origin_countries <- origin_country_counts %>%
  top_n(10, TotalVisitors)

# Filter the long data for these top 10 origin countries
indonesia_data_top10 <- indonesiaData_long %>%
  filter(`Origin.Country` %in% top_10_origin_countries$`Origin.Country`)
write.csv(indonesia_data_top10, "cambodia_data.csv", row.names = FALSE)
# univariate 
#Histogram Plot: Menampilkan distribusi jumlah pengunjung Indonesia.
# Find the smallest and largest values in the Visitors column
min_visitors <- min(indonesia_data_top10$Visitors)
max_visitors <- max(indonesia_data_top10$Visitors)

# Adjusted Plot with dynamic xbins
histogram_plot <- plot_ly(
  data = indonesia_data_top10, 
  x = ~Visitors, 
  type = 'histogram',
  autobinx = FALSE,
  xbins = list(start = min_visitors, end = max_visitors, size = 400000),  # Set an appropriate bin size
  hoverinfo = 'x+y+text',
  text = ~paste("Year:", Year),
  marker = list(
    color = 'rgba(0, 100, 255, 0.7)', 
    line = list(color = 'rgba(0, 0, 0, 1)', width = 1)
  )
) %>%
  layout(
    title = list(text = 'Distribution of Total Indonesian Visitors', font = list(size = 24, color = 'darkblue')),
    xaxis = list(title = 'Number of Visitors', tickangle = -45, tickfont = list(size = 12, color = 'darkblue')),
    yaxis = list(title = 'Count', tickfont = list(size = 12, color = 'darkblue')),
    plot_bgcolor = 'rgba(240, 240, 240, 0.9)',
    paper_bgcolor = 'rgba(255, 255, 255, 1)',
    margin = list(l = 50, r = 50, b = 100, t = 100, pad = 4)
  )

# Display the plot
histogram_plot
#multivarit
#Heatmap Plot: Menampilkan hubungan antara tahun dan negara asal pengunjung dengan menggunakan heatmap plot.

heatmap_plot <- plot_ly(data = indonesia_data_top10, x = ~Year, y = ~interaction(Origin.Country, Destination.Country), z = ~Visitors, type = 'heatmap', colorscale = 'Viridis') %>%
  layout(
    title = list(text = 'Heatmap of Visitors Over Time by Country and Origin', font = list(size = 24, color = 'darkblue')),
    xaxis = list(title = 'Year', tickangle = -45, tickfont = list(size = 12, color = 'darkblue')),
    yaxis = list(title = 'Country and Origin', tickfont = list(size = 12, color = 'darkblue')),
    plot_bgcolor = 'rgba(240, 240, 240, 0.9)',
    paper_bgcolor = 'rgba(255, 255, 255, 1)',
    margin = list(l = 50, r = 50, b = 100, t = 100, pad = 4)
  )

# Display the plot
heatmap_plot
#univariat
# Bar plot using plotly
#Bar Plot: Menampilkan total pengunjung dari top 10 negara asal pengunjung ke Indonesia.
bar_plot <- plot_ly(top_10_origin_countries, x = ~Origin.Country, y = ~TotalVisitors, type = 'bar', 
                    marker = list(color = 'blue')) %>%
  layout(title = 'Total Visitors from Top 10 Origin Countries to Indonesia',
         xaxis = list(title = 'Origin Country', tickangle = -45),
         yaxis = list(title = 'Total Visitors'))

bar_plot
# univariat
# Boxplot Univariat: Menampilkan distribusi jumlah pengunjung Indonesia dengan menggunakan plot boxplot.

box_plot <- plot_ly(indonesia_data_top10, 
                    x = ~`Origin.Country`, 
                    y = ~Visitors, 
                    type = 'box', 
                    boxpoints = 'all', 
                    jitter = 0, 
                    pointpos = 0,
                    marker = list(color = 'rgba(0, 100, 255, 0.5)'),
                    line = list(color = 'rgba(255, 0, 0, 0.5)'),
                    fillcolor = 'rgba(255, 255, 0, 0.5)') %>%
  layout(title = 'Distribution of Visitors by Top 10 Origin Countries Over Years',
         xaxis = list(title = 'Origin Country', tickangle = -45),
         yaxis = list(title = 'Number of Visitors'))

box_plot
#univariat
# Line plot using plotly
#Line Plot: Menampilkan trend jumlah pengunjung Indonesia per tahun untuk top 10 negara asal pengunjung.
line_plot <- plot_ly(indonesia_data_top10, x = ~Year, y = ~Visitors, color = ~`Origin.Country`, 
                     type = 'scatter', mode = 'lines+markers') %>%
  layout(title = 'Trend of Indonesia Visitors Over Years by Top 10 Origin Countries',
         xaxis = list(title = 'Year'),
         yaxis = list(title = 'Number of Visitors'))

line_plot
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
#univariat
# Grafik violin plot
#Violin Plot: Menampilkan distribusi jumlah pengunjung Indonesia dengan memperlihatkan kepadatan dan bentuk distribusi.
violin_plot <- plot_ly(indonesia_data_top10, x = ~Origin.Country, y = ~Visitors, type = 'violin') %>%
  layout(title = 'Violin Plot of Visitors by Origin Country',
         xaxis = list(title = 'Origin Country'),
         yaxis = list(title = 'Number of Visitors'))

violin_plot
#univariat (iseng aja)
# Word Cloud: Menampilkan top 10 negara asal pengunjung dengan menggunakan word cloud.
# Membuat plot untuk top 10 negara asal pengunjung menggunakan word cloud
library(wordcloud)
## Warning: package 'wordcloud' was built under R version 4.3.3
## Loading required package: RColorBrewer
top_10_origin_countries_wordcloud <- indonesia_data_top10 %>%
  group_by(Origin.Country) %>%
  summarise(TotalVisitors = sum(Visitors)) %>%
  arrange(desc(TotalVisitors)) %>%
  top_n(10, TotalVisitors) %>%
  mutate(Origin.Country = as.character(Origin.Country))

wordcloud(top_10_origin_countries_wordcloud$Origin.Country, top_10_origin_countries_wordcloud$TotalVisitors, min.freq = 1, max.words = 10, random.order = FALSE, rot.per = 0.5, use.r.layout = FALSE, main = "Top 10 Origin Countries of Visitors to Indonesia")
## Warning in wordcloud(top_10_origin_countries_wordcloud$Origin.Country,
## top_10_origin_countries_wordcloud$TotalVisitors, : Australia [AU] could not be
## fit on page. It will not be plotted.

# Membuat daftar top 10 negara tujuan yang paling disukai oleh Indonesia

top_10_destination_countries <- indonesia_data_top10 %>%
  group_by(Destination.Country) %>%
  summarise(TotalVisitors = sum(Visitors)) %>%
  arrange(desc(TotalVisitors)) %>%
  top_n(10, TotalVisitors) %>%
  pull(Destination.Country)

# Memfilter data panjang untuk top 10 negara tujuan yang paling disukai oleh Indonesia
filtered_data <- indonesia_data_top10 %>%
  filter(Destination.Country %in% top_10_destination_countries)

# Menampilkan data yang telah difilter
filtered_data
## # A tibble: 60 × 4
##    Destination.Country Origin.Country  Year Visitors
##    <chr>               <chr>          <dbl>    <dbl>
##  1 Indonesia           Australia [AU]  2015  1099058
##  2 Indonesia           Australia [AU]  2016  1302292
##  3 Indonesia           Australia [AU]  2017  1256927
##  4 Indonesia           Australia [AU]  2018  1301478
##  5 Indonesia           Australia [AU]  2019  1386803
##  6 Indonesia           Australia [AU]  2020   256291
##  7 Indonesia           China [CN]      2015  1260700
##  8 Indonesia           China [CN]      2016  1556771
##  9 Indonesia           China [CN]      2017  2093171
## 10 Indonesia           China [CN]      2018  2139161
## # ℹ 50 more rows
library(plotly)

scatter_matrix_plot <- plot_ly(indonesia_data_top10, 
                               type = 'splom', 
                               dimensions = list(
                                 list(label = 'Visitors', 
                                      values = ~Visitors), 
                                 list(label = 'Year', 
                                      values = ~Year), 
                                 list(label = 'Origin Country', 
                                      values = ~Origin.Country)
                               ), 
                               marker = list(showscale = FALSE, 
                                              opacity = 0.7, 
                                              size = 5, 
                                              color = ~Origin.Country, 
                                              colorscale = 'Viridis')) %>%
  layout(title = 'Scatter Matrix Plot of Top 10 Origin Countries')

scatter_matrix_plot
#matriks scatter plot yang menunjukkan hubungan antara variabel Pengunjung, Tahun, dan Negara Asal pada dataset indonesia_data_top10. Plot dapat membantu mengidentifikasi tren atau pola apa pun dalam data, serta korelasi antar variabel.
library(plotly)

scatter_plot_matrix <- plot_ly(indonesia_data_top10, x = ~Visitors, y = ~Year, color = ~Origin.Country, type = 'scatter', mode = 'markers') %>%
  layout(title = 'Scatter Plot Matrix of Visitors, Year, and Origin Country',
         xaxis = list(title = 'Visitors'),
         yaxis = list(title = 'Year'))

scatter_plot_matrix
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
#top 3 visitors to Indonesia with percentages and creates a bar plot to show the results. The plot displays the origin country on the x-axis, the total visitors on the y-axis, and the percentage of visitors from each country as a label on the bar.
# Calculate the top 3 visitors to Indonesia with percentages
top_3_visitors <- indonesia_data_top10 %>%
  group_by(Origin.Country) %>%
  summarise(TotalVisitors = sum(Visitors)) %>%
  arrange(desc(TotalVisitors)) %>%
  top_n(3, TotalVisitors) %>%
  mutate(Percentage = (TotalVisitors / sum(TotalVisitors)) * 100)

# Create a bar plot to show the top 3 visitors to Indonesia with percentages
# Calculate the top 3 years with the most visitors to Indonesia
top_3_years <- indonesia_data_top10 %>%
  group_by(Year) %>%
  summarise(TotalVisitors = sum(Visitors)) %>%
  arrange(desc(TotalVisitors)) %>%
  top_n(3, TotalVisitors)

# Calculate the percentage of each year
top_3_years$Percentage <- (top_3_years$TotalVisitors / sum(indonesia_data_top10$Visitors)) * 100

# Create a bar plot to show the top 3 years with the most visitors to Indonesia
library(ggplot2)
# Calculate the top 3 years with the least visitors to Indonesia
top_3_years <- indonesia_data_top10 %>%
  group_by(Year) %>%
  summarise(TotalVisitors = sum(Visitors)) %>%
  arrange(TotalVisitors) %>%
  top_n(3, TotalVisitors)
# Calculate the top 3 years with the least visitors to Indonesia
top_3_least_years <- indonesia_data_top10 %>%
  group_by(Year) %>%
  summarise(TotalVisitors = sum(Visitors)) %>%
  arrange(TotalVisitors) %>%
  top_n(3, TotalVisitors)

# Create a bar plot to show the top 3 years with the least visitors to Indonesia
# bar plot that shows the top 5 origin countries with the least visitors to Indonesia.
library(plotly)

top_5_least_origin_countries <- indonesia_data_top10 %>%
  group_by(Origin.Country) %>%
  summarise(TotalVisitors = sum(Visitors)) %>%
  arrange(TotalVisitors) %>%
  top_n(5, TotalVisitors)

bar_plot <- plot_ly(top_5_least_origin_countries, x = ~Origin.Country, y = ~TotalVisitors, type = 'bar', 
                    marker = list(color = 'blue')) %>%
  layout(title = 'Top 5 Origin Countries with the Least Visitors to Indonesia',
         xaxis = list(title = 'Origin Country'),
         yaxis = list(title = 'Total Visitors'))

bar_plot
#bar plot that shows the top 5 origin countries with the least visitors to Indonesia.
library(plotly)

top_3_least_years <- indonesia_data_top10 %>%
  group_by(Year) %>%
  summarise(TotalVisitors = sum(Visitors)) %>%
  arrange(TotalVisitors) %>%
  top_n(3, TotalVisitors)

bar_plot <- plot_ly(top_3_least_years, x = ~Year, y = ~TotalVisitors, type = 'bar', 
                    marker = list(color = 'blue')) %>%
  layout(title = 'Top 3 Years with the Least Visitors to Indonesia',
         xaxis = list(title = 'Year'),
         yaxis = list(title = 'Total Visitors'))

bar_plot
# Process the data to get the top 10 origin countries and their percentages
top_10_origin_countries <- indonesia_data_top10 %>%
  group_by(Origin.Country) %>%
  summarise(TotalVisitors = sum(Visitors)) %>%
  arrange(desc(TotalVisitors)) %>%
  top_n(10, TotalVisitors) %>%
  mutate(Percentage = (TotalVisitors / sum(TotalVisitors)) * 100)

# Create the pie chart using Plotly
pie_chart <- plot_ly(
  data = top_10_origin_countries,
  labels = ~Origin.Country,
  values = ~Percentage,
  type = 'pie',
  textinfo = 'label+percent',
  insidetextorientation = 'radial'
) %>%
  layout(
    title = 'Percentage of Visitors from Top 10 Origin Countries to Indonesia',
    showlegend = TRUE
  )

# Display the plot
pie_chart
#Explanation: This pie chart displays the percentage distribution of visitors from the top 10 origin countries. It provides a quick view of the major sources of visitors.
# Data preparation and processing 
data_impute2 <- data_impute[!data_impute$Origin.Country %in% c("Total Intra-ASEAN", "Total Country (World)", "Total EU-27"), ]
data_impute3 <- separate(data_impute2, Origin.Country, into = c("Origin.Country", "Country.Code"), sep = "\\[|\\]")
## Warning: Expected 2 pieces. Additional pieces discarded in 455 rows [1, 2, 3, 4, 5, 6,
## 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
missing_values2 <- sapply(data_impute3, function(x) sum(is.na(x)))
print("Missing values in each feature:")
## [1] "Missing values in each feature:"
print(missing_values2)
## Destination.Country      Origin.Country        Country.Code               X2015 
##                   0                   0                   0                   0 
##               X2016               X2017               X2018               X2019 
##                   0                   0                   0                   0 
##               X2020 
##                   0
library(plotly)
library(rjson)
library(dplyr)
library(countrycode)
## Warning: package 'countrycode' was built under R version 4.3.3
# Convert Country.Code to ISO3 
data_impute3 <- data_impute3 %>%
  mutate(ISO3 = countrycode(Country.Code, "iso2c", "iso3c"))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ISO3 = countrycode(Country.Code, "iso2c", "iso3c")`.
## Caused by warning:
## ! Some values were not matched unambiguously: O0, ZB
# Check for any NA values in the ISO3 column
missing_iso3 <- data_impute3 %>% filter(is.na(ISO3))
print(missing_iso3)
##    Destination.Country                Origin.Country Country.Code   X2015
## 1    Brunei Darussalam Others Unspecified Countries            O0    3435
## 2             Cambodia Others Unspecified Countries            O0   18037
## 3             Cambodia        Serbia and Montenegro            ZB     863
## 4            Indonesia Others Unspecified Countries            O0  580132
## 5              Lao PDR Others Unspecified Countries            O0   37524
## 6             Malaysia Others Unspecified Countries            O0  320900
## 7          Philippines Others Unspecified Countries            O0  271037
## 8            Singapore Others Unspecified Countries            O0  278200
## 9             Thailand Others Unspecified Countries            O0 1124238
## 10            Viet Nam Others Unspecified Countries            O0  299075
##      X2016   X2017   X2018   X2019  X2020 ISO3
## 1     3273    3854    4113    9581    825 <NA>
## 2    65755   39245   18788    6006   1120 <NA>
## 3     1052    1191    1249    1202    523 <NA>
## 4   709585    7455    6340    6573   1412 <NA>
## 5    43197   49211   34650   37412  48353 <NA>
## 6  1223374  130433  420230  440935  50108 <NA>
## 7   257991  210534  154380  171283 114314 <NA>
## 8   395117  438535  479029  636449 131649 <NA>
## 9  1308314 1386184 1406413 1418321 350954 <NA>
## 10  544458  256166  381612  514324 190276 <NA>
data_impute4 <- na.omit(data_impute3)


data_aggregated <- data_impute4 %>%
  mutate(Total_Visitors = X2015 + X2016 + X2017 + X2018 + X2019 + X2020) %>%
  select(Destination.Country, Origin.Country, ISO3, Total_Visitors)

# Create the interactive choropleth map
map <- plot_ly(data = data_aggregated, 
               type = 'choropleth', 
               locations = ~ISO3, 
               z = ~Total_Visitors, 
               text = ~paste("Origin Country:", Origin.Country, "<br>",
                             "Total Visitors:", Total_Visitors),
               colorscale = "Viridis",
               colorbar = list(title = "Total Visitors"))

# Configure the layout
map <- map %>%
  layout(title = 'Total Visitors by Origin Country to Destination Country (2015-2020)',
         geo = list(showframe = FALSE,
                    showcoastlines = FALSE,
                    projection = list(type = 'equirectangular')))

# Display the map
map
write.csv(data_impute4, "data.csv", row.names = FALSE)